2 citations · 2 across the 2 of their papers we have counts for
3 papers
Geometry Meets Physics: Data-Efficient Pre-Training for Unstructured Neural PDE Solvers
Luis Medrano-Navarro, Giacomo Baldan, Qiang Liu +4
Neural surrogate models for Partial Differential Equations (PDEs) on unstructured 3D geometries are often limited by poor generalization and the high cost of generating large-scale…
Physics-Constrained Adaptive Flow Matching for Climate Downscaling
Kevin Debeire, Aytaç Paçal, Pierre Gentine +3
Regional climate information at kilometer scales is essential for assessing the impacts of climate change, but generating it with global climate models is too expensive due to thei…
Guiding diffusion models to reconstruct flow fields from sparse data
Marc Amorós-Trepat, Luis Medrano-Navarro, Qiang Liu +2
The reconstruction of unsteady flow fields from limited measurements is a challenging and crucial task for many engineering applications. Machine learning models are gaining popula…